Motor Current Time-varying Quadratic Phase Coupling Analysis and its Application in Traction Motor Fault Detection under Varying-speed Condition

The locomotive traction motors convert electrical energy into mechanical energy and are the power source of the train. Therefore, their working condition is vital to the performance and security of the entire train. Motor current signature analysis (MCSA) has been widely used in motor fault detectio...

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Bibliographic Details
Published in:IEEE sensors journal 2024-04, Vol.24 (8), p.1-1
Main Authors: Yang, Jiangtian, Li, Zeyang, Zhang, Peng, Zhang, Kun, Xu, Yonggang
Format: Article
Language:English
Subjects:
Fault detection
Fault Diagnosis
Faults
Harmonics
Instantaneous Wavelet Bicoherence
Motor Current Signature Analysis
Power sources
Quadratic Phase Coupling
Signature analysis
Stators
Trains
Variable-speed Conditions
Wavelet bispectrum entropy
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Summary:The locomotive traction motors convert electrical energy into mechanical energy and are the power source of the train. Therefore, their working condition is vital to the performance and security of the entire train. Motor current signature analysis (MCSA) has been widely used in motor fault detection because of its low cost and accessibility. The classic MCSA based on residual harmonics is only suitable for motor diagnosis under uniform speed conditions. When the locomotive is running on railway line, the speed cannot be kept constant. Moreover, the reflects of mechanical faults into the stator current are typically very subtle and easy to be disturbed in industrial field. To overcome these limitations, a novel motor fault characteristic extraction method based on the phase relations of harmonic component in the stator current is proposed. The relationship between faults and phase coupling of the harmonics under variable-speed conditions are analyzed and the time-varying quadratic phase coupling (QPC) models are presented. Then, the instantaneous wavelet bicoherence is employed to analyze the motor current signal and the fault-related QPC was extracted. Furthermore, wavelet bispectrum entropy is proposed to describe the uniformity of QPC at the bifrequency domain, which can indicate the severity of failure from an unusual perspective. The proposed approach was effectively applied in the locomotive online operation test, and the faults of the traction motor was successfully diagnosed.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3371491